{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sigmoid 函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sigmoid(t):\n",
    "    return 1. / (1. + np.exp(-t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHsNJREFUeJzt3Xl8XHW9//HXJ5M06ZLu6b5CNwqlpQ1FkQt4QShVqYAi\nuCL6Q+5P/Ln8ror6u+p9+Lj3IfrTe/V3WawI6nUpLgiIxbK4gAvYfUlL23ShTZqk6Zq0aZaZ+fz+\nmGkZQtJM05mcmTPv5+ORzlm+k/PJmcy7J99z5nvM3RERkXApCroAERHJPIW7iEgIKdxFREJI4S4i\nEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCaHioDY8cuRInzJlSlCbFxHJS6tXrz7g7hU9tQss\n3KdMmcKqVauC2ryISF4ys1fSaaduGRGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCaEew93MHjKz/Wa2\nqZv1ZmbfMbNqM9tgZvMzX6aIiJyJdI7cfwAsOs3664Dpya87gPvPviwRETkbPV7n7u7Pm9mU0zRZ\nAvzIE/fre9HMhprZWHevy1CNIhJS7k5bNE5rR4zWjjgdsTjRuBM99ehE44npjlic2Kllr7aJu+MO\njhOPg0NyWWJ5/OQ6J2XZax9fXZ9Yllrfq9MpdXe57PRtUxdWThnO5TN6/BzSWcnEh5jGA3tT5muS\ny14X7mZ2B4mjeyZNmpSBTYtIUNydptYojc2t7G9u4+CxdppaO2g6EU0+dtDUGqU5Od3SHjsV5Cc6\nYrR2JOYL6TbOZonHO684Ny/CPW3uvhRYClBZWVlAL6lI/nF36o628srBFvYcOp58bKH2yAn2N7XR\neKyN9mi8y+cWFxlD+pdQXlbM4OTj8IGl9O8Xoay4iLKSyKnp0pII/UsilJVEKIkYJZEiIkVGScSI\nFBVRHDGKi4zibqaLigwDiswwe/XRzCgyMJKPqetPti/idc+FVx/h1UCGRNtXl1sXy16/PiiZCPda\nYGLK/ITkMhHJE9FYnC11zWyoPcLLdc1sqWtia30zzW3RU22Ki4zxw/ozYVh/Fk4dzqjyUipSvkYO\nKmVI/xIGl5VQVlIUeLgVukyE+xPAXWa2DLgEOKr+dpHcFos762uO8Py2RlbtPsyaPYdpaY8BUF5a\nzKyx5bzjovHMHFPOlBEDmTxiAGOHlFEc0dXT+aLHcDeznwFXAiPNrAb4MlAC4O4PAMuBxUA10AJ8\nKFvFikjvtXbEeG7Lfp7d0sCftjVy6Hg7RQazxgzmnQsmsGDyMOZPGsaEYf111B0C6Vwtc2sP6x34\nWMYqEpGMcXde2nWIX62u4alN9RxrizJsQAlXzhzFm2eN4orpFQwZUBJ0mZIFgQ35KyLZ09oR4/F1\ntTz8l928XN/MoNJirrtgDDdcNJ5LzhlBpEhH5mGncBcJkdaOGD95aQ/3/aGag8fbmTWmnK/fdCFv\nnzuO/v0iQZcnfUjhLhICsbjzy9V7+c9nt1N3tJU3TRvBx948jTeeM0L95wVK4S6S5zbVHuWLj21i\n/d4jzJs4lG++ay6XThsZdFkSMIW7SJ5qj8b51jPbWPr8DoYP7Me3b5nH9XPH6UhdAIW7SF7a0XiM\nTyxby6baJt5dOZEvLD5PV73IayjcRfLM7zbV8+mfr6O0uIjvvn8B154/JuiSJAcp3EXyRDzu/L/f\nV/Mfz25j7sShfPd9CxgzpCzosiRHKdxF8kA0Fuezv9rAo2tqufGi8fz7jXMoK9GljdI9hbtIjmuL\nxvj4T9fy9OYGPnX1DP7XVdN00lR6pHAXyWFt0Rgf+eEqXth+gK+8fTa3vWlq0CVJnlC4i+SoaCzO\nJ362jhe2H+DrN13IzRdP7PlJIkkav1MkB8Xjzucf3cjvqur50ttmK9jljCncRXLQt5/bzi9W1/CJ\nq6Zz+2XqipEzp3AXyTHLN9bx7ee2884FE/jk1dODLkfylMJdJIdU7TvK//75euZPGsq/3XCBroqR\nXlO4i+SI5tYO/unHaxjSv4QH3r+A0mJdxy69p6tlRHLEvzy2iZrDLfz8o29kVLk+eSpnR0fuIjng\n0TU1PLZuH5+8egaVU4YHXY6EgMJdJGB7D7XwL49tYuHU4XzszdOCLkdCQuEuEiB35wu/3oiZ8R/v\nnqd7m0rGKNxFAvSrNbW8sP0An1s0k/FD+wddjoSIwl0kII3NbXz1yc1UTh7Gey+ZHHQ5EjIKd5GA\n/PvyLZxoj/G1my6kSN0xkmEKd5EArNlzmF+vreV/XD6VaaMGBV2OhJDCXaSPxePOv/5mM6PKS/mf\nV+rqGMkOhbtIH3tsXS3r9x7hc4tmMbBUnyOU7FC4i/ShE+0x7vndy8ydOJQbLhofdDkSYgp3kT70\no7/tpqGpjf/z1vN0ElWySuEu0keaWzu4/087uGJGBRdriAHJsrTC3cwWmdlWM6s2s7u7WD/EzH5j\nZuvNrMrMPpT5UkXy20N/3s2Rlg7++ZqZQZciBaDHcDezCHAvcB0wG7jVzGZ3avYxYLO7zwWuBL5p\nZv0yXKtI3jrS0s6DL+zk2vNHM2fCkKDLkQKQzpH7QqDa3Xe6ezuwDFjSqY0D5Za4s8Ag4BAQzWil\nInnswRd2caw9yqfeMiPoUqRApBPu44G9KfM1yWWp/gs4D9gHbAQ+4e7xjFQokueaWzv40d92s+j8\nMcwaMzjocqRAZOqE6rXAOmAcMA/4LzN73W+xmd1hZqvMbFVjY2OGNi2S23729z00tUa584pzgy5F\nCkg64V4LTEyZn5BclupDwKOeUA3sAmZ1/kbuvtTdK929sqKiorc1i+SNtmiMB1/YxZumjWDuxKFB\nlyMFJJ1wXwlMN7OpyZOktwBPdGqzB7gKwMxGAzOBnZksVCQf/XpNLfub2/inKzTMgPStHj/77O5R\nM7sLWAFEgIfcvcrM7kyufwD4KvADM9sIGPA5dz+QxbpFcl487nz3+Z1cOGEIb5o2IuhypMCkNbCF\nuy8Hlnda9kDK9D7gmsyWJpLf/rStkV0HjvOdWy8icSGZSN/RJ1RFsuThv+5m9OBSrrtgTNClSAFS\nuItkQfX+Yzy/rZH3XTKZkojeZtL39FsnkgU/+ttu+kWKuPWSSUGXIgVK4S6SYU2tHfxydQ1vnzuO\nkYNKgy5HCpTCXSTDHl1dQ0t7jNsunRJ0KVLAFO4iGeTuLFu5lwsnDNEAYRIohbtIBq2vOcrL9c28\n++KJPTcWySKFu0gGPbJyD/1LIlw/d1zQpUiBU7iLZMjxtihPrNvH2y4cS3lZSdDlSIFTuItkyJMb\n9nG8PcYtC9UlI8FTuItkyLKVe5k2ahDzJw0LuhQRhbtIJmytb2btniPccvFEjSMjOUHhLpIBv1y9\nl+Ii44aLOt+kTCQYCneRsxSLO4+v28eVM0cxQp9IlRyhcBc5S3/bcZD9zW3cOF9H7ZI7FO4iZ+nR\ntTWUlxbzj7NGBV2KyCkKd5Gz0NIeZcWmehbPGUtZSSTockROUbiLnIVnNjdwvD3GO3QiVXKMwl3k\nLDy2tpZxQ8q4ZOrwoEsReQ2Fu0gvHTjWxvPbD7DkovEUFenadsktCneRXnpy/T5icde17ZKTFO4i\nvfTbjXXMHF3OjNHlQZci8joKd5FeqD/aysrdh3nbhWODLkWkSwp3kV5YvrEOgMUKd8lRCneRXvjt\nxjpmjSnn3IpBQZci0iWFu8gZqjt6gtWvqEtGcpvCXeQMLd9YD8DiOQp3yV0Kd5Ez9NsN+5g9djDn\nqEtGcpjCXeQM7DtygjV7jvBWdclIjlO4i5yBU1fJqEtGcpzCXeQM/HZjHeePG8zUkQODLkXktNIK\ndzNbZGZbzazazO7ups2VZrbOzKrM7E+ZLVMkePuOnGDtniM6ape8UNxTAzOLAPcCbwFqgJVm9oS7\nb05pMxS4D1jk7nvMTHctkNB5ZnMDANddMCbgSkR6ls6R+0Kg2t13uns7sAxY0qnNe4BH3X0PgLvv\nz2yZIsF7enM900YN0lUykhfSCffxwN6U+ZrkslQzgGFm9kczW21mH+jqG5nZHWa2ysxWNTY29q5i\nkQAcbengxZ2HuGb26KBLEUlLpk6oFgMLgLcC1wL/YmYzOjdy96XuXunulRUVFRnatEj2/X5rA7G4\nc8356pKR/NBjnztQC0xMmZ+QXJaqBjjo7seB42b2PDAX2JaRKkUC9nRVA6MHl3Lh+CFBlyKSlnSO\n3FcC081sqpn1A24BnujU5nHgMjMrNrMBwCXAlsyWKhKM1o4Yf9rWyFtmj9YdlyRv9Hjk7u5RM7sL\nWAFEgIfcvcrM7kyuf8Ddt5jZ74ANQBx40N03ZbNwkb7yl+oDtLTHuGa2umQkf6TTLYO7LweWd1r2\nQKf5bwDfyFxpIrnh6aoGykuLecM5I4IuRSRt+oSqyGnE4s6zWxp486xR9CvW20Xyh35bRU5jzZ7D\nHDzezjXn6xJIyS8Kd5HTeLqqnn6RIq6YoUt3Jb8o3EW64e48vbmBS6eNoLysJOhyRM6Iwl2kG9sa\njvHKwRZdJSN5SeEu0o2nq+oxg6tnaxw8yT8Kd5FuPL25gYsmDmVUeVnQpYicMYW7SBf2HTnBxtqj\nGktG8pbCXaQLJ8du1yiQkq8U7iJd0Njtku8U7iKdaOx2CQOFu0gnGrtdwkDhLtKJxm6XMFC4i6TQ\n2O0SFgp3kRQau13CQuEukkJjt0tYKNxFkjR2u4SJfoNFkjR2u4SJwl0kSWO3S5go3EVIjN2+oqqB\nN56rsdslHBTuIsDWhmb2HGrhWn1wSUJC4S5C4ioZjd0uYaJwFwFWVNUzf9Iwjd0uoaFwl4JXc7iF\nqn1NGihMQkXhLgXv1Njt6m+XEFG4S8FbUVXPjNGDmDpyYNCliGSMwl0K2uHj7fx91yGNJSOho3CX\ngvbcy/uJO/pUqoSOwl0K2oqqesYOKWOOxm6XkFG4S8E60R7jhe2NXDN7NGYau13CJa1wN7NFZrbV\nzKrN7O7TtLvYzKJm9s7MlSiSHc9vb6S1I66rZCSUegx3M4sA9wLXAbOBW81sdjft7gGeznSRItmw\noqqeIf1LWDh1eNCliGRcOkfuC4Fqd9/p7u3AMmBJF+0+DvwK2J/B+kSyIhqL89yW/Vw1axQlEfVO\nSvik81s9HtibMl+TXHaKmY0HbgDuz1xpItnz912HOHqiQ1fJSGhl6pDlP4HPuXv8dI3M7A4zW2Vm\nqxobGzO0aZEzt6KqntLiIi7X2O0SUsVptKkFJqbMT0guS1UJLEtecTASWGxmUXd/LLWRuy8FlgJU\nVlZ6b4sWORvxuPPUpnqunFnBgH7pvAVE8k86v9krgelmNpVEqN8CvCe1gbtPPTltZj8Anuwc7CK5\nYvWew+xvbmPxnLFBlyKSNT2Gu7tHzewuYAUQAR5y9yozuzO5/oEs1yiSUb/dUEe/4iKuOk/97RJe\naf1N6u7LgeWdlnUZ6u5+29mXJZIdiS6ZOq6YUcGgUnXJSHjpGjApKGv2HKahqY23qktGQk7hLgVl\n+cb6ZJeMbqcn4aZwl4Jxskvm8ukVlJeVBF2OSFYp3KVgrN17hLqjrSyeo7FkJPwU7lIwlm+so1+k\niKt1r1QpAAp3KQjuzlMb6/iH6SMZrC4ZKQAKdykIa/ceYd/RVn1wSQqGwl0KwuNra+lXXKSBwqRg\nKNwl9DpicZ7cUMfV543SVTJSMBTuEnp/rj7AwePtLJk3vufGIiGhcJfQe3xtLYPLirlypob3lcKh\ncJdQa2mP8vTmBt564VhKiyNBlyPSZxTuEmrPbG6gpT2mLhkpOAp3CbXH1+1j7JAyFk7RTbClsCjc\nJbQOHW/n+W2NXD93HEVFFnQ5In1K4S6h9fi6WqJx5x0XqUtGCo/CXULJ3Xlk5V7mjB/CeWMHB12O\nSJ9TuEsobapt4uX6Zm6+eGLPjUVCSOEuofTIqj2UFhdx/dxxQZciEgiFu4ROa0eMx9ft47oLxjCk\nv4YbkMKkcJfQWVFVT3NrlJsr1SUjhUvhLqHzyMq9TBzenzecMyLoUkQCo3CXUNl7qIW/7jjIzQsm\n6tp2KWgKdwmVn7y0hyKDmxZMCLoUkUAp3CU0WjtiPLJyD9fMHsO4of2DLkckUAp3CY0nN9RxuKWD\nD7xxctCliARO4S6h8d9/2820UYN447k6kSqicJdQWLf3COtrjvKBN07GTCdSRRTuEgo//OtuBpUW\nc+N8nUgVAYW7hEDd0RP8Zv0+3rlgAoNKi4MuRyQnKNwl7z38l9048OHLpgZdikjOSCvczWyRmW01\ns2ozu7uL9e81sw1mttHM/mpmczNfqsjrNbV28NOX9rB4zlgmDh8QdDkiOaPHcDezCHAvcB0wG7jV\nzGZ3arYLuMLd5wBfBZZmulCRrvz0pT0ca4vy0cvPCboUkZySzpH7QqDa3Xe6ezuwDFiS2sDd/+ru\nh5OzLwI6qyVZ1x6N8/BfdnHpuSO4YPyQoMsRySnphPt4YG/KfE1yWXc+DDzV1Qozu8PMVpnZqsbG\nxvSrFOnCL1bvpaGpjY9ecW7QpYjknIyeUDWzN5MI9891td7dl7p7pbtXVlRUZHLTUmDaojHu/X01\n8yYO5fLpI4MuRyTnpBPutUDqwNgTkstew8wuBB4Elrj7wcyUJ9K1n6+qYd/RVj71lhn60JJIF9IJ\n95XAdDObamb9gFuAJ1IbmNkk4FHg/e6+LfNliryqLRrjvj9UM3+SjtpFutPjJz7cPWpmdwErgAjw\nkLtXmdmdyfUPAF8CRgD3JY+iou5emb2ypZA9snIvdUdb+fo7L9RRu0g30vo4n7svB5Z3WvZAyvRH\ngI9ktjSR12tu7eDbz25n4ZThXDZNR+0i3dFntSWv3P/HHRw83s5Dt52no3aR09DwA5I3ao+c4Pt/\n3sWSeeOYO3Fo0OWI5DSFu+SNb/zuZRz4zLUzgy5FJOcp3CUvrNp9iMfW7ePDl01lwjCNISPSE4W7\n5Lz2aJwv/Hoj44f25643Twu6HJG8oBOqkvO+98JOtjUc4/sfrGSgxmsXSYuO3CWnvXLwON95bjvX\nXTCGq84bHXQ5InlD4S45KxZ3PvPLDZREivjy288PuhyRvKK/cSVnLX1+J3/fdYhvvmsuY4aUBV2O\nSF7RkbvkpE21R/nWM1t565yx3Dj/dCNMi0hXFO6Sc461RfnEsrUMH9iPf7vhAn0SVaQX1C0jOcXd\n+cwv1rPrwHF+/JFLGDqgX9AlieQlHblLTrn/Tzt4alM9X1h8Hpeeq4HBRHpL4S454/cvN/CNFVt5\n+9xxfPiyqUGXI5LXFO6SE9buOczHfrKW88cN5p6b5qifXeQsKdwlcDsaj3H7D1YyanApD9+2kAH9\ndCpI5Gwp3CVQuw4c530PvkSkyPjR7QupKC8NuiSRUNAhkgRme0Mz73nwJWJx58cfvoTJIwYGXZJI\naCjcJRAbao7woYdXYmYsu+MNzBhdHnRJIqGibhnpc7/bVMfN3/0bZSURHvmogl0kG3TkLn0mHnfu\n+2M1//fpbVw0aShL31+pPnaRLFG4S5/Y39zKpx9Zz5+rD7Bk3jjuuelCykoiQZclEloKd8kqd+ep\nTfV86fFNHGuL8rUb5/DuiyfqOnaRLFO4S9bsO3KCLz9RxTObG7hg/GC+dfM89a+L9BGFu2Tc0RMd\n3P/HHTz8l12YwRcWz+L2N02lOKLz9yJ9ReEuGXO0pYMfv/QK33thJ0dPdHDDvPF8+poZTBg2IOjS\nRAqOwl3O2u4Dx/nBX3fz81V7aWmPccWMCj67aCbnjxsSdGkiBUvhLr3S1NrBUxvr+OXqGlbuPkxx\nkXH9vHF85LJzmD1ucNDliRQ8hbukreZwC89t2c+zWxp4cedBOmLOORUD+eyimdw0fwKjB+s+pyK5\nQuEuXXJ39hxqYeXuw6zcdYi/7z7ErgPHATinYiC3v2kqiy4Yw7yJQ3VZo0gOSivczWwR8G0gAjzo\n7l/rtN6S6xcDLcBt7r4mw7VKFrg7B4+388rB4+xoPM6Wuiaq9jWxpa6J5tYoAEMHlHDxlOG895JJ\n/OOsUZxTMSjgqkWkJz2Gu5lFgHuBtwA1wEoze8LdN6c0uw6Ynvy6BLg/+SgBisedptYO9je30dDU\nyv6mtlenm1t55WALrxxs4Vhb9NRz+pdEOG9sOUvmjWP22CEsmDyM6aMGUVSko3ORfJLOkftCoNrd\ndwKY2TJgCZAa7kuAH7m7Ay+a2VAzG+vudRmvOA+5O9G4E4snHqOx+GvmYzEnGo8n151cHqcj5rR2\nxGjtiHGiI0ZbR5wTKfMnlx1vi3L0RAdNrR00nXh1+lhbFPfX11NeWkzF4FImDx/AxVOGM3nEAKaM\nGMiUkQOZNHwAEQW5SN5LJ9zHA3tT5mt4/VF5V23GAxkP9z9u3c9Xn0z8v+LJf5xEgJ5c5g6OJx5T\nws3dT61PtE22ObUs+T1Svqefem7KvJOy3F+zzdTn4xCNx4l3EbCZ0C9SRFlJEQP6FTOkfwlD+pcw\nbmgZs8aWM7is5NSyivJSRg8uY1R5KaMGl+pORyIFoE/f5WZ2B3AHwKRJk3r1PcrLSpg1ZjAkDy4t\n8X2Tj69fhkFyCjNOtXvNsmTDk+tJafPaZfa6da9uL2WbKdstiRiRIqO4yIgUFSUfjeKIUdxp/mS7\n4qIiIpHEdP+SCGWnvopeM68jbBHpTjrhXgtMTJmfkFx2pm1w96XAUoDKyspeHc8umDyMBZOH9eap\nIiIFI53BPlYC081sqpn1A24BnujU5gngA5bwBuCo+ttFRILT45G7u0fN7C5gBYlLIR9y9yozuzO5\n/gFgOYnLIKtJXAr5oeyVLCIiPUmrz93dl5MI8NRlD6RMO/CxzJYmIiK9pTFYRURCSOEuIhJCCncR\nkRBSuIuIhJDCXUQkhMy7GnykLzZs1gi80sunjwQOZLCcTMnVuiB3a1NdZ0Z1nZkw1jXZ3St6ahRY\nuJ8NM1vl7pVB19FZrtYFuVub6jozquvMFHJd6pYREQkhhbuISAjla7gvDbqAbuRqXZC7tamuM6O6\nzkzB1pWXfe4iInJ6+XrkLiIip5Gz4W5m7zKzKjOLm1llp3WfN7NqM9tqZtd28/zhZvaMmW1PPmZ8\nEHgze8TM1iW/dpvZum7a7Tazjcl2qzJdRxfb+4qZ1abUtribdouS+7DazO7ug7q+YWYvm9kGM/u1\nmQ3tpl2f7K+efv7kENbfSa7fYGbzs1VLyjYnmtkfzGxz8vf/E120udLMjqa8vl/Kdl0p2z7taxPQ\nPpuZsi/WmVmTmX2yU5s+2Wdm9pCZ7TezTSnL0sqijL8f3T0nv4DzgJnAH4HKlOWzgfVAKTAV2AFE\nunj+14G7k9N3A/dkud5vAl/qZt1uYGQf7ruvAP/cQ5tIct+dA/RL7tPZWa7rGqA4OX1Pd69JX+yv\ndH5+EsNYP0XiZltvAF7qg9duLDA/OV0ObOuiriuBJ/vq9+lMXpsg9lkXr2s9iWvB+3yfAZcD84FN\nKct6zKJsvB9z9sjd3be4+9YuVi0Blrl7m7vvIjGG/MJu2v0wOf1D4B3ZqTRxtALcDPwsW9vIglM3\nPnf3duDkjc+zxt2fdvdocvZFEnfsCko6P/+pG7+7+4vAUDMbm82i3L3O3dckp5uBLSTuR5wv+nyf\ndXIVsMPde/sBybPi7s8DhzotTieLMv5+zNlwP43ubsbd2Wh/9W5Q9cDoLNb0D0CDu2/vZr0Dz5rZ\n6uR9ZPvCx5N/Fj/UzZ+B6e7HbLmdxBFeV/pif6Xz8we6j8xsCnAR8FIXqy9Nvr5Pmdn5fVUTPb82\nQf9e3UL3B1lB7bN0sijj+61Pb5DdmZk9C4zpYtUX3f3xTG3H3d3MenVZUJo13srpj9ovc/daMxsF\nPGNmLyf/h++109UF3A98lcQb8askuoxuP5vtZaKuk/vLzL4IRIGfdPNtMr6/8o2ZDQJ+BXzS3Zs6\nrV4DTHL3Y8nzKY8B0/uotJx9bSxxG9Drgc93sTrIfXbK2WTRmQo03N396l48La2bcQMNZjbW3euS\nfxbuz0aNZlYM3AgsOM33qE0+7jezX5P4E+ys3hDp7jsz+x7wZBer0t2PGa3LzG4D3gZc5cnOxi6+\nR8b3VxcyduP3TDOzEhLB/hN3f7Tz+tSwd/flZnafmY1096yPoZLGaxPIPku6Dljj7g2dVwS5z0gv\nizK+3/KxW+YJ4BYzKzWzqST+9/17N+0+mJz+IJCxvwQ6uRp42d1rulppZgPNrPzkNImTipu6apsp\nnfo4b+hme+nc+DzTdS0CPgtc7+4t3bTpq/2Vkzd+T56/+T6wxd2/1U2bMcl2mNlCEu/jg9msK7mt\ndF6bPt9nKbr9CzqofZaUThZl/v2Y7bPHvf0iEUo1QBvQAKxIWfdFEmeWtwLXpSx/kOSVNcAI4Dlg\nO/AsMDxLdf4AuLPTsnHA8uT0OSTOfK8Hqkh0T2R73/03sBHYkPwFGdu5ruT8YhJXY+zoo7qqSfQr\nrkt+PRDk/urq5wfuPPl6krji497k+o2kXLWVxZouI9GdtiFlPy3uVNddyX2znsSJ6UuzXdfpXpug\n91lyuwNJhPWQlGV9vs9I/OdSB3Qk8+vD3WVRtt+P+oSqiEgI5WO3jIiI9EDhLiISQgp3EZEQUriL\niISQwl1EJIQU7iIiIaRwFxEJIYW7iEgI/X/WUUwLSSLVMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f23668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 500)\n",
    "\n",
    "plt.plot(x, sigmoid(x))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
